🏥 Hospital Brain Tumor Detection System

A comprehensive desktop application for detecting brain tumors from MRI scans using machine learning, with a role-based hospital management system interface.

✨ Features

• Multi-User System with different roles (Admin, Doctor, Radiologist, Technician)
• Multiple ML Models (SVM, Random Forest, Logistic Regression) for tumor detection
• Modern UI with customizable themes and responsive design
• Patient Management with complete medical record keeping
• PDF Report Generation for professional documentation
• Model Performance Comparison with visual analytics
• Image Annotation capabilities for radiologists

📦 Installation

1. Clone the repository:

   git clone https://github.com/itxawangee/hospital-brain-tumor-detection.git
   cd hospital-brain-tumor-detection

2. Install required dependencies:

   pip install -r requirements.txt

3. Create necessary directories:

   mkdir -p patient_data patient_images models dataset/yes dataset/no

🔧 Requirements

• Python 3.8+
• Required packages:

  numpy
  pandas
  scikit-learn
  opencv-python
  pillow
  matplotlib
  fpdf2
  joblib
  

🚀 Usage

Run the application:

python brain_tumor_detection.py

Login Credentials:

Role	Username	Password
Admin	admin	admin123
Doctor	doctor	doctor123
Radiologist	radiologist	radio123
Technician	technician	tech123

🧠 Machine Learning Models

The system implements three classification models:

1. Support Vector Machine (SVM)

   • Pros: Effective in high dimensional spaces
   • Cons: Not suitable for large datasets

2. Random Forest

   • Pros: Handles large datasets well
   • Cons: Slower prediction time

3. Logistic Regression

   • Pros: Simple and efficient
   • Cons: Struggles with non-linear boundaries

📂 Project Structure

hospital-brain-tumor-detection/
├── brain_tumor_detection.py    # Main application file
├── requirements.txt            # Dependency list
├── patient_data/               # Patient records storage
├── patient_images/             # Uploaded MRI scans
├── models/                     # Trained model storage
├── dataset/                    # Training dataset
│   ├── yes/                    # Tumor-positive samples
│   └── no/                     # Tumor-negative samples

🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the project
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

Distributed under the MIT License. See LICENSE for more information.

📧 Contact

Project Maintainer: Akrash Noor - akrashnoor2580@gmail.com

Project Link: https://github.com/itxawangee/hospital-brain-tumor-detection

### Key Features of this README:

1. **Badges** - Visual indicators for Python version, GUI framework, and ML capabilities
2. **Clear Installation** - Step-by-step setup instructions
3. **Visual Hierarchy** - Organized sections with emoji icons
4. **Login Credentials** - Ready-to-use test accounts
5. **Project Structure** - Clear directory layout
6. **ML Model Documentation** - Quick comparison of implemented algorithms
7. **Contribution Guidelines** - Standard GitHub workflow instructions

To use this README:
1. Replace placeholder values (yourusername, your.name, etc.)
2. Add actual screenshots to the screenshots folder
3. Update the requirements.txt with your exact dependencies
4. Include a LICENSE file if using a different license